In Degrees All

SQL function: cugraph_in_degrees_all

Compute in-degree for every vertex.

Signature

cugraph_in_degrees_all(table_name [, src_col, dst_col [, weight_col [, options_json]]])

Allowed argument counts: 1, 3, 4, 5.

Quickstart

SELECT * FROM cugraph_in_degrees_all('target_edges')

Positional arguments

Argument	Type	Required	Default	Notes
table_name	Utf8	yes
src_col	Utf8	no	src
dst_col	Utf8	no	dst
weight_col	Utf8|null	no		accepted as an edge-column binding; native algorithm execution does not consume weights; semantic effect: none for this algorithm
options_json	Utf8	no

JSON options

This algorithm has no algorithm-specific options.

Graph construction options

Shared by all cuGraph functions, shown here with this function's defaults. The construction_policy option controls whether Nexus requests Python cuGraph-compatible edge normalization or bypasses it for raw libcugraph-style construction; see graph construction options for the full policy guide.

Option	Type	Default	Constraints	Description
construction_policy	Utf8	"python_cugraph"	one of "python_cugraph", "raw_libcugraph"	Edge-list construction semantics used before calling libcugraph.
directed	Boolean	true		Whether graph construction treats edges as directed.
renumber	Boolean	true		Whether graph construction may renumber external vertex identifiers internally.

Output schema

Column	Type	Nullable	Description
vertex	Int64	no	Vertex whose in-degree count is reported.
in_degree	Int64	no	Number of incoming edges for the vertex.

note

These are the generic registry schemas. Run cugraph_validate_call for the concrete, table-specific output schema of a particular call.

Examples

This example runs on the citation network demo dataset.

In-graph citations vs. what the metadata claims

papers.n_citation is AMiner's global citation count; in_degree counts only edges present in this corpus. Joining the two measures how much of each paper's audience lies outside the dataset:

SELECT p.year, d.in_degree AS in_graph, p.n_citation AS reported,
       p.n_citation - d.in_degree AS outside,
       p.title AS paper
FROM cugraph_in_degrees_all('citation_edges', 'src', 'dst') d
JOIN papers p ON p.paper_id = d.vertex
ORDER BY d.in_degree DESC
LIMIT 5;

year	in_graph	reported	outside	paper
2004	22,892	35,541	12,649	Distinctive Image Features from Scale-Invariant Keypoints
2011	19,662	31,047	11,385	LIBSVM: A library for support vector machines
1989	17,207	44,175	26,968	Genetic algorithms in search, optimization, and machine learning
1996	16,579	42,437	25,858	Fuzzy sets
2001	15,048	34,741	19,693	Random Forests

SIFT is the most-cited paper within the corpus. Older cross-disciplinary classics (genetic algorithms, fuzzy sets) show the biggest gap — more than half their citations come from fields this CS-centric corpus doesn't cover.

Limitations & notes

• dry-run validates table resolution, column presence, static dtypes, and options only
• dry-run does not scan edge data, construct a graph, or prove source-vertex existence

Validate before running

Always dry-run a call before executing it. Validation checks the function, table, columns, dtypes, and options without touching the GPU:

SELECT * FROM cugraph_validate_call(
  'cugraph_in_degrees_all',
  'your_edges_table',
  '{"src_col":"src","dst_col":"dst"}'
);

See Discovery & validation for the full cugraph_validate_call contract.